LLM integration into systems you already run
LLM integration connects a model to your applications, data, and access controls so it can work inside real workflows. You receive a gateway layer with pinned model versions, provider fallback, and rate limits; structured output through JSON schema or function calling, so responses arrive in a shape your code can use; identity passthrough, so the model sees only what the requesting user may see; and call-level tracing with OpenTelemetry or Langfuse. Input and output checks built with NeMo Guardrails or Llama Guard screen for prompt injection and data leakage, and the design follows the OWASP Top 10 for LLM Applications. The result is an LLM your operations and security teams can approve for production.
Prompt engineering and context engineering
Prompt engineering is the design and testing of the instructions, examples, and output formats a model receives. Context engineering decides what information enters each call and in what order: instructions, reference passages, tool results, conversation history, and user-specific permissions, all within a token budget. Both matter because longer context is never free. Cost and latency rise with every token, and models can overlook details buried deep in a long input. We keep stable instructions at the front so providers can cache them, trim history the task does not need, and test every prompt version with Promptfoo or DeepEval. You receive a prompt library you can change safely, with a test suite that shows what each change did.
LLM fine-tuning with LoRA and QLoRA
LLM fine-tuning further trains a model on your examples to change how it behaves: a required output format, your terminology, a house tone, or a classification scheme. It is a poor way to teach facts that change, which belong in retrieval. We use supervised fine-tuning and preference tuning with Hugging Face TRL, and train LoRA or QLoRA adapters on open-weight families such as Llama, Mistral, Qwen, and Gemma using Axolotl or Unsloth. Adapters are small files, and one base model served with vLLM can host several of them. You receive the curated dataset, the trained adapter, and a report comparing the tuned model against the base model and the best prompt-only result. If the tuned model does not win, the report says so, and you do not deploy it.
Model distillation for cost and latency
Model distillation trains a smaller student model on the outputs of a larger teacher model for one narrow task. It pays off when a task is stable, request volume is high, and latency or self-hosting requirements rule out a large model on every call. Before starting, we confirm that the teacher model’s license and provider terms permit using its output to train another model, because several do not allow every use. The student model is scored on the same evaluation set as the teacher, and it ships only if it stays inside the accuracy tolerance you agreed in advance. You get a model you can run at a fraction of the size, with evidence of exactly what it gave up.
